Overview

Dataset statistics

Number of variables22
Number of observations5000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory859.5 KiB
Average record size in memory176.0 B

Variable types

Numeric16
Categorical6

Alerts

number_vmail_messages is highly correlated with voice_mail_plan_no and 1 other fieldsHigh correlation
total_day_minutes is highly correlated with total_day_chargeHigh correlation
total_day_charge is highly correlated with total_day_minutesHigh correlation
total_eve_minutes is highly correlated with total_eve_chargeHigh correlation
total_eve_charge is highly correlated with total_eve_minutesHigh correlation
total_night_minutes is highly correlated with total_night_chargeHigh correlation
total_night_charge is highly correlated with total_night_minutesHigh correlation
total_intl_minutes is highly correlated with total_intl_chargeHigh correlation
total_intl_charge is highly correlated with total_intl_minutesHigh correlation
international_plan_no is highly correlated with international_plan_yesHigh correlation
international_plan_yes is highly correlated with international_plan_noHigh correlation
voice_mail_plan_no is highly correlated with number_vmail_messages and 1 other fieldsHigh correlation
voice_mail_plan_yes is highly correlated with number_vmail_messages and 1 other fieldsHigh correlation
number_vmail_messages is highly correlated with voice_mail_plan_no and 1 other fieldsHigh correlation
total_day_minutes is highly correlated with total_day_chargeHigh correlation
total_day_charge is highly correlated with total_day_minutesHigh correlation
total_eve_minutes is highly correlated with total_eve_chargeHigh correlation
total_eve_charge is highly correlated with total_eve_minutesHigh correlation
total_night_minutes is highly correlated with total_night_chargeHigh correlation
total_night_charge is highly correlated with total_night_minutesHigh correlation
total_intl_minutes is highly correlated with total_intl_chargeHigh correlation
total_intl_charge is highly correlated with total_intl_minutesHigh correlation
international_plan_no is highly correlated with international_plan_yesHigh correlation
international_plan_yes is highly correlated with international_plan_noHigh correlation
voice_mail_plan_no is highly correlated with number_vmail_messages and 1 other fieldsHigh correlation
voice_mail_plan_yes is highly correlated with number_vmail_messages and 1 other fieldsHigh correlation
number_vmail_messages is highly correlated with voice_mail_plan_no and 1 other fieldsHigh correlation
total_day_minutes is highly correlated with total_day_chargeHigh correlation
total_day_charge is highly correlated with total_day_minutesHigh correlation
total_eve_minutes is highly correlated with total_eve_chargeHigh correlation
total_eve_charge is highly correlated with total_eve_minutesHigh correlation
total_night_minutes is highly correlated with total_night_chargeHigh correlation
total_night_charge is highly correlated with total_night_minutesHigh correlation
total_intl_minutes is highly correlated with total_intl_chargeHigh correlation
total_intl_charge is highly correlated with total_intl_minutesHigh correlation
international_plan_no is highly correlated with international_plan_yesHigh correlation
international_plan_yes is highly correlated with international_plan_noHigh correlation
voice_mail_plan_no is highly correlated with number_vmail_messages and 1 other fieldsHigh correlation
voice_mail_plan_yes is highly correlated with number_vmail_messages and 1 other fieldsHigh correlation
international_plan_no is highly correlated with international_plan_yesHigh correlation
voice_mail_plan_no is highly correlated with voice_mail_plan_yesHigh correlation
voice_mail_plan_yes is highly correlated with voice_mail_plan_noHigh correlation
international_plan_yes is highly correlated with international_plan_noHigh correlation
number_vmail_messages is highly correlated with voice_mail_plan_no and 1 other fieldsHigh correlation
total_day_minutes is highly correlated with total_day_chargeHigh correlation
total_day_charge is highly correlated with total_day_minutesHigh correlation
total_eve_minutes is highly correlated with total_eve_chargeHigh correlation
total_eve_charge is highly correlated with total_eve_minutesHigh correlation
total_night_minutes is highly correlated with total_night_chargeHigh correlation
total_night_charge is highly correlated with total_night_minutesHigh correlation
total_intl_minutes is highly correlated with total_intl_chargeHigh correlation
total_intl_charge is highly correlated with total_intl_minutesHigh correlation
international_plan_no is highly correlated with international_plan_yesHigh correlation
international_plan_yes is highly correlated with international_plan_noHigh correlation
voice_mail_plan_no is highly correlated with number_vmail_messages and 1 other fieldsHigh correlation
voice_mail_plan_yes is highly correlated with number_vmail_messages and 1 other fieldsHigh correlation
state has 72 (1.4%) zeros Zeros
number_vmail_messages has 3678 (73.6%) zeros Zeros
number_customer_service_calls has 1023 (20.5%) zeros Zeros

Reproduction

Analysis started2022-06-10 17:17:17.882613
Analysis finished2022-06-10 17:17:48.635295
Duration30.75 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

state
Real number (ℝ≥0)

ZEROS

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.519968
Minimum0
Maximum1
Zeros72
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:48.720039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.26
median0.52
Q30.78
95-th percentile0.98
Maximum1
Range1
Interquartile range (IQR)0.52

Descriptive statistics

Standard deviation0.296069604
Coefficient of variation (CV)0.5693996631
Kurtosis-1.185987798
Mean0.519968
Median Absolute Deviation (MAD)0.26
Skewness-0.05797513823
Sum2599.84
Variance0.08765721042
MonotonicityNot monotonic
2022-06-10T14:17:49.239894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.98158
 
3.2%
0.46125
 
2.5%
0.02124
 
2.5%
0.26119
 
2.4%
0.9118
 
2.4%
0.7116
 
2.3%
0.86116
 
2.3%
1115
 
2.3%
0.68114
 
2.3%
0.74114
 
2.3%
Other values (41)3781
75.6%
ValueCountFrequency (%)
072
1.4%
0.02124
2.5%
0.0492
1.8%
0.0689
1.8%
0.0852
1.0%
0.196
1.9%
0.1299
2.0%
0.1488
1.8%
0.1694
1.9%
0.1890
1.8%
ValueCountFrequency (%)
1115
2.3%
0.98158
3.2%
0.96106
2.1%
0.9498
2.0%
0.92101
2.0%
0.9118
2.4%
0.88112
2.2%
0.86116
2.3%
0.8489
1.8%
0.8285
1.7%

account_length
Real number (ℝ≥0)

Distinct218
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4101595041
Minimum0
Maximum1
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:49.349638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1404958678
Q10.2975206612
median0.4090909091
Q30.520661157
95-th percentile0.6859504132
Maximum1
Range1
Interquartile range (IQR)0.2231404959

Descriptive statistics

Standard deviation0.1640271056
Coefficient of variation (CV)0.3999105322
Kurtosis-0.1016210812
Mean0.4101595041
Median Absolute Deviation (MAD)0.1115702479
Skewness0.1092911238
Sum2050.797521
Variance0.02690489136
MonotonicityNot monotonic
2022-06-10T14:17:49.457367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.36776859565
 
1.3%
0.355371900859
 
1.2%
0.429752066157
 
1.1%
0.380165289357
 
1.1%
0.45867768656
 
1.1%
0.413223140555
 
1.1%
0.409090909155
 
1.1%
0.351239669455
 
1.1%
0.475206611654
 
1.1%
0.421487603354
 
1.1%
Other values (208)4433
88.7%
ValueCountFrequency (%)
011
0.2%
0.0041322314052
 
< 0.1%
0.008264462818
0.2%
0.012396694213
 
0.1%
0.016528925622
 
< 0.1%
0.020661157022
 
< 0.1%
0.024793388435
0.1%
0.028925619832
 
< 0.1%
0.033057851243
 
0.1%
0.037190082643
 
0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.9793388431
 
< 0.1%
0.9586776861
 
< 0.1%
0.95454545452
< 0.1%
0.92561983472
< 0.1%
0.92148760332
< 0.1%
0.91322314052
< 0.1%
0.90909090911
 
< 0.1%
0.89256198353
0.1%
0.88842975211
 
< 0.1%

area_code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
0.06862745098039191
2495 
0.0
1259 
1.0
1246 

Length

Max length19
Median length3
Mean length10.984
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.06862745098039191
2nd row0.06862745098039191
3rd row0.06862745098039191
4th row0.0
5th row0.06862745098039191

Common Values

ValueCountFrequency (%)
0.068627450980391912495
49.9%
0.01259
25.2%
1.01246
24.9%

Length

2022-06-10T14:17:49.552262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-10T14:17:49.604126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.068627450980391912495
49.9%
0.01259
25.2%
1.01246
24.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_vmail_messages
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct48
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1491384615
Minimum0
Maximum1
Zeros3678
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:49.672124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3269230769
95-th percentile0.7115384615
Maximum1
Range1
Interquartile range (IQR)0.3269230769

Descriptive statistics

Standard deviation0.2605075652
Coefficient of variation (CV)1.746749715
Kurtosis0.1991271752
Mean0.1491384615
Median Absolute Deviation (MAD)0
Skewness1.350493197
Sum745.6923077
Variance0.06786419154
MonotonicityNot monotonic
2022-06-10T14:17:49.776197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
03678
73.6%
0.596153846283
 
1.7%
0.538461538567
 
1.3%
0.557692307767
 
1.3%
0.634615384666
 
1.3%
0.461538461564
 
1.3%
0.519230769264
 
1.3%
0.576923076958
 
1.2%
0.558
 
1.2%
0.615384615457
 
1.1%
Other values (38)738
 
14.8%
ValueCountFrequency (%)
03678
73.6%
0.076923076921
 
< 0.1%
0.11538461542
 
< 0.1%
0.15384615382
 
< 0.1%
0.17307692312
 
< 0.1%
0.19230769234
 
0.1%
0.21153846152
 
< 0.1%
0.230769230811
 
0.2%
0.254
 
0.1%
0.26923076929
 
0.2%
ValueCountFrequency (%)
11
 
< 0.1%
0.98076923081
 
< 0.1%
0.96153846152
 
< 0.1%
0.94230769233
 
0.1%
0.92307692315
 
0.1%
0.90384615384
 
0.1%
0.88461538468
0.2%
0.865384615411
0.2%
0.84615384629
0.2%
0.826923076916
0.3%

total_day_minutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1961
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5129129445
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:49.881563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2608819346
Q10.4088193457
median0.5123755334
Q30.6150782361
95-th percentile0.7712802276
Maximum1
Range1
Interquartile range (IQR)0.2062588905

Descriptive statistics

Standard deviation0.1533277359
Coefficient of variation (CV)0.2989352044
Kurtosis-0.02129447073
Mean0.5129129445
Median Absolute Deviation (MAD)0.1032716927
Skewness-0.01173082717
Sum2564.564723
Variance0.0235093946
MonotonicityNot monotonic
2022-06-10T14:17:49.984735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.538549075410
 
0.2%
0.438122332910
 
0.2%
0.4537695599
 
0.2%
0.51209103849
 
0.2%
0.52489331449
 
0.2%
0.49644381229
 
0.2%
0.50384068289
 
0.2%
0.52176386918
 
0.2%
0.53997155058
 
0.2%
0.6133712668
 
0.2%
Other values (1951)4911
98.2%
ValueCountFrequency (%)
02
< 0.1%
0.0073968705551
< 0.1%
0.018776671411
< 0.1%
0.020483641541
< 0.1%
0.022190611661
< 0.1%
0.022475106691
< 0.1%
0.035561877671
< 0.1%
0.050071123761
< 0.1%
0.053769559031
< 0.1%
0.055476529161
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99800853491
< 0.1%
0.9866287341
< 0.1%
0.98236130871
< 0.1%
0.96273115221
< 0.1%
0.9598862021
< 0.1%
0.95448079661
< 0.1%
0.95106685631
< 0.1%
0.9470839261
< 0.1%
0.94480796591
< 0.1%

total_day_calls
Real number (ℝ≥0)

Distinct123
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6062387879
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:50.089803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4060606061
Q10.5272727273
median0.6060606061
Q30.6848484848
95-th percentile0.8060606061
Maximum1
Range1
Interquartile range (IQR)0.1575757576

Descriptive statistics

Standard deviation0.1201890752
Coefficient of variation (CV)0.1982536876
Kurtosis0.1785677943
Mean0.6062387879
Median Absolute Deviation (MAD)0.07878787879
Skewness-0.08489096367
Sum3031.193939
Variance0.01444541381
MonotonicityNot monotonic
2022-06-10T14:17:50.197912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6363636364117
 
2.3%
0.6181818182113
 
2.3%
0.5757575758108
 
2.2%
0.5696969697104
 
2.1%
0.5878787879104
 
2.1%
0.6060606061102
 
2.0%
0.6666666667101
 
2.0%
0.6787878788101
 
2.0%
0.5575757576100
 
2.0%
0.6545454545100
 
2.0%
Other values (113)3950
79.0%
ValueCountFrequency (%)
02
< 0.1%
0.18181818181
 
< 0.1%
0.20606060611
 
< 0.1%
0.21212121211
 
< 0.1%
0.21818181821
 
< 0.1%
0.23636363642
< 0.1%
0.24242424242
< 0.1%
0.25454545452
< 0.1%
0.26666666674
0.1%
0.27272727273
0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.98787878791
 
< 0.1%
0.96969696972
 
< 0.1%
0.95757575763
0.1%
0.95151515152
 
< 0.1%
0.94545454553
0.1%
0.92121212122
 
< 0.1%
0.91515151527
0.1%
0.90909090916
0.1%
0.9030303032
 
< 0.1%

total_day_charge
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1961
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5128793173
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:50.307471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2608768407
Q10.4088018742
median0.5123828648
Q30.6149598394
95-th percentile0.7712600402
Maximum1
Range1
Interquartile range (IQR)0.2061579652

Descriptive statistics

Standard deviation0.1533144025
Coefficient of variation (CV)0.298928807
Kurtosis-0.02116592527
Mean0.5128793173
Median Absolute Deviation (MAD)0.1032463186
Skewness-0.01172900707
Sum2564.396586
Variance0.023505306
MonotonicityNot monotonic
2022-06-10T14:17:50.411559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.538487282510
 
0.2%
0.43808567610
 
0.2%
0.4538152619
 
0.2%
0.51204819289
 
0.2%
0.52493306569
 
0.2%
0.49648594389
 
0.2%
0.50384872829
 
0.2%
0.52175368148
 
0.2%
0.53999330668
 
0.2%
0.61328647938
 
0.2%
Other values (1951)4911
98.2%
ValueCountFrequency (%)
02
< 0.1%
0.0073627844711
< 0.1%
0.01874163321
< 0.1%
0.020414993311
< 0.1%
0.022255689421
< 0.1%
0.022423025441
< 0.1%
0.035642570281
< 0.1%
0.05003346721
< 0.1%
0.053714859441
< 0.1%
0.055555555561
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99799196791
< 0.1%
0.98661311911
< 0.1%
0.98226238291
< 0.1%
0.96268406961
< 0.1%
0.95983935741
< 0.1%
0.95448460511
< 0.1%
0.95097054891
< 0.1%
0.94695448461
< 0.1%
0.94477911651
< 0.1%

total_eve_minutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1879
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5516540005
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:50.515472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3258042343
Q10.457451196
median0.5526532857
Q30.6436623591
95-th percentile0.7800934836
Maximum1
Range1
Interquartile range (IQR)0.186211163

Descriptive statistics

Standard deviation0.1389917761
Coefficient of variation (CV)0.2519546237
Kurtosis0.05137513056
Mean0.5516540005
Median Absolute Deviation (MAD)0.09348364036
Skewness-0.01101769459
Sum2758.270003
Variance0.01931871382
MonotonicityNot monotonic
2022-06-10T14:17:50.619684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.467143249910
 
0.2%
0.549078911210
 
0.2%
0.634863898810
 
0.2%
0.46081935669
 
0.2%
0.57904866659
 
0.2%
0.59527082769
 
0.2%
0.51910915599
 
0.2%
0.51553478149
 
0.2%
0.61451745949
 
0.2%
0.53340665389
 
0.2%
Other values (1869)4907
98.1%
ValueCountFrequency (%)
01
< 0.1%
0.061314271
< 0.1%
0.085784987631
< 0.1%
0.10393181191
< 0.1%
0.11465493541
< 0.1%
0.11602969481
< 0.1%
0.11685455051
< 0.1%
0.12070387681
< 0.1%
0.13005224092
< 0.1%
0.13225185591
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99477591421
< 0.1%
0.98790211711
< 0.1%
0.97387957111
< 0.1%
0.96810558151
< 0.1%
0.96673082211
< 0.1%
0.96480615891
< 0.1%
0.96370635141
< 0.1%
0.96068188071
< 0.1%
0.95930712131
< 0.1%

total_eve_calls
Real number (ℝ≥0)

Distinct126
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5893588235
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:50.725910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3941176471
Q10.5117647059
median0.5882352941
Q30.6705882353
95-th percentile0.7823529412
Maximum1
Range1
Interquartile range (IQR)0.1588235294

Descriptive statistics

Standard deviation0.1166264461
Coefficient of variation (CV)0.1978869942
Kurtosis0.1173634027
Mean0.5893588235
Median Absolute Deviation (MAD)0.07647058824
Skewness-0.02017520328
Sum2946.794118
Variance0.01360172792
MonotonicityNot monotonic
2022-06-10T14:17:50.833080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6176470588115
 
2.3%
0.5705882353110
 
2.2%
0.5352941176110
 
2.2%
0.5529411765106
 
2.1%
0.6058823529106
 
2.1%
0.5941176471104
 
2.1%
0.5647058824100
 
2.0%
0.6117647059100
 
2.0%
0.699
 
2.0%
0.576470588299
 
2.0%
Other values (116)3951
79.0%
ValueCountFrequency (%)
01
 
< 0.1%
0.070588235291
 
< 0.1%
0.21176470591
 
< 0.1%
0.21764705881
 
< 0.1%
0.22352941181
 
< 0.1%
0.24705882351
 
< 0.1%
0.25294117651
 
< 0.1%
0.25882352942
 
< 0.1%
0.26470588241
 
< 0.1%
0.27058823535
0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.99411764711
 
< 0.1%
0.98823529411
 
< 0.1%
0.96470588241
 
< 0.1%
0.93529411761
 
< 0.1%
0.92352941181
 
< 0.1%
0.91764705881
 
< 0.1%
0.91176470595
0.1%
0.90588235294
0.1%
0.91
 
< 0.1%

total_eve_charge
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1659
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5517412488
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:50.938921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3257683598
Q10.4574571336
median0.5528955031
Q30.643804594
95-th percentile0.7800711744
Maximum1
Range1
Interquartile range (IQR)0.1863474604

Descriptive statistics

Standard deviation0.13901143
Coefficient of variation (CV)0.251950403
Kurtosis0.0512887853
Mean0.5517412488
Median Absolute Deviation (MAD)0.09349725008
Skewness-0.01099032836
Sum2758.706244
Variance0.01932417767
MonotonicityNot monotonic
2022-06-10T14:17:51.225685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.514396635415
 
0.3%
0.461015852515
 
0.3%
0.521514073114
 
0.3%
0.607893885513
 
0.3%
0.549013264313
 
0.3%
0.613393723713
 
0.3%
0.627952119112
 
0.2%
0.552895503111
 
0.2%
0.543513426111
 
0.2%
0.602394047211
 
0.2%
Other values (1649)4872
97.4%
ValueCountFrequency (%)
01
< 0.1%
0.061468780331
< 0.1%
0.085732772571
< 0.1%
0.10384988681
< 0.1%
0.11452604341
< 0.1%
0.11614364281
< 0.1%
0.11679068261
< 0.1%
0.12067292141
< 0.1%
0.13005499842
< 0.1%
0.13231963771
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99482368171
< 0.1%
0.98802976381
< 0.1%
0.97411840831
< 0.1%
0.96829505011
< 0.1%
0.96700097061
< 0.1%
0.96505985121
< 0.1%
0.96376577161
< 0.1%
0.96085409251
< 0.1%
0.95956001291
< 0.1%

total_night_minutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1853
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.507320557
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:51.377676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2972025316
Q10.4225316456
median0.5073417722
Q30.5941772152
95-th percentile0.7174810127
Maximum1
Range1
Interquartile range (IQR)0.1716455696

Descriptive statistics

Standard deviation0.1279184538
Coefficient of variation (CV)0.2521452207
Kurtosis0.08235919689
Mean0.507320557
Median Absolute Deviation (MAD)0.08556962025
Skewness0.01932491656
Sum2536.602785
Variance0.01636313083
MonotonicityNot monotonic
2022-06-10T14:17:51.511907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.476455696211
 
0.2%
0.491898734211
 
0.2%
0.471392405111
 
0.2%
0.543291139210
 
0.2%
0.528860759510
 
0.2%
0.577468354410
 
0.2%
0.53164556969
 
0.2%
0.48784810139
 
0.2%
0.49012658239
 
0.2%
0.54354430389
 
0.2%
Other values (1843)4901
98.0%
ValueCountFrequency (%)
01
< 0.1%
0.058734177221
< 0.1%
0.11063291141
< 0.1%
0.11392405061
< 0.1%
0.11822784811
< 0.1%
0.121
< 0.1%
0.1268354432
< 0.1%
0.12886075951
< 0.1%
0.13493670891
< 0.1%
0.13670886081
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.9668354431
< 0.1%
0.96607594941
< 0.1%
0.95569620251
< 0.1%
0.93088607591
< 0.1%
0.92379746841
< 0.1%
0.9222784811
< 0.1%
0.91113924051
< 0.1%
0.89898734181
< 0.1%
0.89848101271
< 0.1%

total_night_calls
Real number (ℝ≥0)

Distinct131
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5709668571
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:51.615714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3828571429
Q10.4971428571
median0.5714285714
Q30.6457142857
95-th percentile0.7542857143
Maximum1
Range1
Interquartile range (IQR)0.1485714286

Descriptive statistics

Standard deviation0.1140496335
Coefficient of variation (CV)0.1997482552
Kurtosis0.1444380753
Mean0.5709668571
Median Absolute Deviation (MAD)0.07428571429
Skewness0.002132842744
Sum2854.834286
Variance0.0130073189
MonotonicityNot monotonic
2022-06-10T14:17:51.723215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6121
 
2.4%
0.5828571429109
 
2.2%
0.5714285714108
 
2.2%
0.5942857143106
 
2.1%
0.5657142857105
 
2.1%
0.5885714286104
 
2.1%
0.52103
 
2.1%
0.5371428571103
 
2.1%
0.5428571429102
 
2.0%
0.56102
 
2.0%
Other values (121)3937
78.7%
ValueCountFrequency (%)
01
 
< 0.1%
0.068571428571
 
< 0.1%
0.18857142861
 
< 0.1%
0.20571428571
 
< 0.1%
0.21714285712
< 0.1%
0.22857142861
 
< 0.1%
0.23428571431
 
< 0.1%
0.244
0.1%
0.24571428571
 
< 0.1%
0.25142857141
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.97142857141
< 0.1%
0.961
< 0.1%
0.94857142861
< 0.1%
0.94285714291
< 0.1%
0.93714285711
< 0.1%
0.921
< 0.1%
0.91428571431
< 0.1%
0.90857142862
< 0.1%
0.90285714292
< 0.1%

total_night_charge
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1028
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5074694429
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:51.827785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2971299944
Q10.4226223973
median0.5075970737
Q30.5942599887
95-th percentile0.7175295442
Maximum1
Range1
Interquartile range (IQR)0.1716375914

Descriptive statistics

Standard deviation0.1279551298
Coefficient of variation (CV)0.2521435163
Kurtosis0.08237761539
Mean0.5074694429
Median Absolute Deviation (MAD)0.08553742262
Skewness0.01928674434
Sum2537.347214
Variance0.01637251523
MonotonicityNot monotonic
2022-06-10T14:17:51.932283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.543612830619
 
0.4%
0.476646032619
 
0.4%
0.607765897618
 
0.4%
0.54192459218
 
0.4%
0.458638154218
 
0.4%
0.528981429418
 
0.4%
0.577377602718
 
0.4%
0.531795160417
 
0.3%
0.590320765317
 
0.3%
0.582442318516
 
0.3%
Other values (1018)4822
96.4%
ValueCountFrequency (%)
01
< 0.1%
0.058525604951
< 0.1%
0.11086100171
< 0.1%
0.11423747891
< 0.1%
0.11817670231
< 0.1%
0.11986494091
< 0.1%
0.12661789532
< 0.1%
0.12886888011
< 0.1%
0.13505908841
< 0.1%
0.1367473271
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.96736072031
< 0.1%
0.96623522791
< 0.1%
0.95610579631
< 0.1%
0.93134496341
< 0.1%
0.92402926281
< 0.1%
0.92234102421
< 0.1%
0.91164884641
< 0.1%
0.89926842991
< 0.1%
0.89870568371
< 0.1%

total_intl_minutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct170
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.513089
Minimum0
Maximum1
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:52.036251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.285
Q10.425
median0.515
Q30.6
95-th percentile0.735
Maximum1
Range1
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.1380697857
Coefficient of variation (CV)0.2690951974
Kurtosis0.6553166102
Mean0.513089
Median Absolute Deviation (MAD)0.09
Skewness-0.2099662929
Sum2565.445
Variance0.01906326573
MonotonicityNot monotonic
2022-06-10T14:17:52.149984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.55590
 
1.8%
0.4988
 
1.8%
0.56583
 
1.7%
0.5781
 
1.6%
0.50581
 
1.6%
0.54580
 
1.6%
0.48579
 
1.6%
0.5378
 
1.6%
0.5578
 
1.6%
0.52578
 
1.6%
Other values (160)4184
83.7%
ValueCountFrequency (%)
024
0.5%
0.021
 
< 0.1%
0.0552
 
< 0.1%
0.0651
 
< 0.1%
0.13
 
0.1%
0.1052
 
< 0.1%
0.112
 
< 0.1%
0.121
 
< 0.1%
0.1251
 
< 0.1%
0.131
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.9852
< 0.1%
0.9651
< 0.1%
0.961
< 0.1%
0.9452
< 0.1%
0.9351
< 0.1%
0.9251
< 0.1%
0.921
< 0.1%
0.9151
< 0.1%
0.912
< 0.1%

total_intl_calls
Real number (ℝ≥0)

Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22176
Minimum0
Maximum1
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:52.245558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.15
median0.2
Q30.3
95-th percentile0.45
Maximum1
Range1
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.1228394086
Coefficient of variation (CV)0.5539295121
Kurtosis3.268183647
Mean0.22176
Median Absolute Deviation (MAD)0.05
Skewness1.360692479
Sum1108.8
Variance0.0150895203
MonotonicityNot monotonic
2022-06-10T14:17:52.325149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.15992
19.8%
0.2953
19.1%
0.1743
14.9%
0.25706
14.1%
0.3495
9.9%
0.35308
 
6.2%
0.05265
 
5.3%
0.4172
 
3.4%
0.45148
 
3.0%
0.576
 
1.5%
Other values (11)142
 
2.8%
ValueCountFrequency (%)
024
 
0.5%
0.05265
 
5.3%
0.1743
14.9%
0.15992
19.8%
0.2953
19.1%
0.25706
14.1%
0.3495
9.9%
0.35308
 
6.2%
0.4172
 
3.4%
0.45148
 
3.0%
ValueCountFrequency (%)
11
 
< 0.1%
0.952
 
< 0.1%
0.94
 
0.1%
0.852
 
< 0.1%
0.87
 
0.1%
0.759
 
0.2%
0.76
 
0.1%
0.6519
0.4%
0.623
0.5%
0.5545
0.9%

total_intl_charge
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct170
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5131844444
Minimum0
Maximum1
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:52.416923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2851851852
Q10.4259259259
median0.5148148148
Q30.6
95-th percentile0.7351851852
Maximum1
Range1
Interquartile range (IQR)0.1740740741

Descriptive statistics

Standard deviation0.1380580939
Coefficient of variation (CV)0.269022367
Kurtosis0.6559885458
Mean0.5131844444
Median Absolute Deviation (MAD)0.08888888889
Skewness-0.2102861147
Sum2565.922222
Variance0.0190600373
MonotonicityNot monotonic
2022-06-10T14:17:52.522926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.555555555690
 
1.8%
0.490740740788
 
1.8%
0.564814814883
 
1.7%
0.570370370481
 
1.6%
0.505555555681
 
1.6%
0.544444444480
 
1.6%
0.485185185279
 
1.6%
0.529629629678
 
1.6%
0.5578
 
1.6%
0.525925925978
 
1.6%
Other values (160)4184
83.7%
ValueCountFrequency (%)
024
0.5%
0.020370370371
 
< 0.1%
0.055555555562
 
< 0.1%
0.064814814811
 
< 0.1%
0.13
 
0.1%
0.10555555562
 
< 0.1%
0.10925925932
 
< 0.1%
0.12037037041
 
< 0.1%
0.12592592591
 
< 0.1%
0.12962962961
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.98518518522
< 0.1%
0.96481481481
< 0.1%
0.95925925931
< 0.1%
0.94444444442
< 0.1%
0.93518518521
< 0.1%
0.92592592591
< 0.1%
0.92037037041
< 0.1%
0.91481481481
< 0.1%
0.90925925932
< 0.1%

number_customer_service_calls
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1744888889
Minimum0
Maximum1
Zeros1023
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-10T14:17:52.610315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1111111111
median0.1111111111
Q30.2222222222
95-th percentile0.4444444444
Maximum1
Range1
Interquartile range (IQR)0.1111111111

Descriptive statistics

Standard deviation0.1451514814
Coefficient of variation (CV)0.8318666153
Kurtosis1.48109554
Mean0.1744888889
Median Absolute Deviation (MAD)0.1111111111
Skewness1.04246233
Sum872.4444444
Variance0.02106895256
MonotonicityNot monotonic
2022-06-10T14:17:52.684682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.11111111111786
35.7%
0.22222222221127
22.5%
01023
20.5%
0.3333333333665
 
13.3%
0.4444444444252
 
5.0%
0.555555555696
 
1.9%
0.666666666734
 
0.7%
0.777777777813
 
0.3%
12
 
< 0.1%
0.88888888892
 
< 0.1%
ValueCountFrequency (%)
01023
20.5%
0.11111111111786
35.7%
0.22222222221127
22.5%
0.3333333333665
 
13.3%
0.4444444444252
 
5.0%
0.555555555696
 
1.9%
0.666666666734
 
0.7%
0.777777777813
 
0.3%
0.88888888892
 
< 0.1%
12
 
< 0.1%
ValueCountFrequency (%)
12
 
< 0.1%
0.88888888892
 
< 0.1%
0.777777777813
 
0.3%
0.666666666734
 
0.7%
0.555555555696
 
1.9%
0.4444444444252
 
5.0%
0.3333333333665
 
13.3%
0.22222222221127
22.5%
0.11111111111786
35.7%
01023
20.5%

churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
0.0
4293 
1.0
707 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04293
85.9%
1.0707
 
14.1%

Length

2022-06-10T14:17:52.760208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-10T14:17:52.807584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.04293
85.9%
1.0707
 
14.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

international_plan_no
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
1.0
4527 
0.0
473 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.04527
90.5%
0.0473
 
9.5%

Length

2022-06-10T14:17:52.855141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-10T14:17:52.901895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.04527
90.5%
0.0473
 
9.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

international_plan_yes
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
0.0
4527 
1.0
473 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.04527
90.5%
1.0473
 
9.5%

Length

2022-06-10T14:17:52.949525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-10T14:17:52.997101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.04527
90.5%
1.0473
 
9.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

voice_mail_plan_no
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
1.0
3677 
0.0
1323 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.03677
73.5%
0.01323
 
26.5%

Length

2022-06-10T14:17:53.177459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-10T14:17:53.223910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.03677
73.5%
0.01323
 
26.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

voice_mail_plan_yes
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
0.0
3677 
1.0
1323 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03677
73.5%
1.01323
 
26.5%

Length

2022-06-10T14:17:53.270365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-10T14:17:53.316745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.03677
73.5%
1.01323
 
26.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-06-10T14:17:45.936655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:18.977774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:21.278889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.705573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:24.248994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:25.757382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:28.070268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:30.119519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:31.859952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.496320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:35.386889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:37.156548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:39.341050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:41.000382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:42.794120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.313078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:46.028625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:19.110968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:21.392817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.796145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:24.336194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:25.863136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:28.197320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:30.243518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:31.943987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.590746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:35.498739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:37.295303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:39.426447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:41.135005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:42.877593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.402034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:46.131214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:19.237580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:21.517293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.882177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:24.425509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:25.975610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:28.362602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:30.372487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:32.030055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.691990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:35.592729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:37.411037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:39.510191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:41.252986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:42.963490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.485878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:46.226780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:19.330420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:21.600204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.976927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:24.507037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:26.111514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:28.491132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:30.468289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:32.116729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.785270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:35.688623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:37.525091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:39.602446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:41.346965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:43.051695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.572893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:46.334710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:19.445916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:21.685062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.091929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:24.592919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:26.234244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:28.616565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:30.555955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:32.235102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.880973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:35.780892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:37.635013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:39.691315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:41.432165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:43.143650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.663422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:46.456703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:19.542457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:21.771175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.189637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:24.681592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:26.343544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:28.758956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:30.641146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:32.349937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.967863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:35.865396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:37.764895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:39.785697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:41.544280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:43.229160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.752732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:46.619853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:19.643140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:21.852888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.276350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:24.785442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:26.462768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:28.886879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:30.725913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:32.461230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:34.051531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:35.950265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:37.885209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:39.900378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:41.665098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:43.314464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.848660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:46.797984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:19.755232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:21.933779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.363706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:24.871923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:26.633127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:29.026705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:30.826124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:32.577903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:34.134109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:36.045629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:38.009348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:40.018790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:41.781950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:43.397502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.942188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:46.953426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:19.847471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.015197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.445600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:24.957678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:26.813997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:29.166374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:30.942073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:32.699602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:34.218085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:36.151069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:38.142570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:40.117521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:41.899994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:43.480775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:45.036109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:47.082139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:19.953139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.101487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.526847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:25.048719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:26.919728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:29.284715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:31.069381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:32.819637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:34.303173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:36.260680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:38.266220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:40.228912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:41.992511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:43.565013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:45.326385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:47.230278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:20.060976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.190154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.608757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:25.138865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:27.046043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:29.417273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:31.181375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:32.912044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:34.392838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:36.352484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:38.416790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:40.359982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:42.264701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:43.652843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:45.412146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:47.359927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:20.166164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.284661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.697418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:25.250459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:27.185275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:29.532073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:31.271290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.002644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:34.495400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:36.442453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:38.874725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:40.461972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:42.359898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:43.750134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:45.511414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:47.467466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:20.281604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.370230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.792923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:25.351126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:27.296520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:29.632986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:31.361429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.091125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:34.960786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:36.539951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:38.960898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:40.551712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:42.446275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:43.863495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:45.600970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:47.571010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:20.399533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.453327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.874770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:25.436742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:27.409701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:29.761166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:31.585057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.183710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:35.050720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:36.673600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:39.052160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:40.647018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:42.530243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.015180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:45.686711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:47.711298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:20.560531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.537549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:23.956780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:25.533874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:27.870654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:29.882736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:31.675175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.267794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:35.155664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:36.876689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:39.140942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:40.748913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:42.621843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.129530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:45.770496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:47.820205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:20.692530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:22.620068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:24.165527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:25.643618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:27.954100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:29.999157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:31.773896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:33.371975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:35.276861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:37.015630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:39.230468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:40.875756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:42.709711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:44.228229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-10T14:17:45.852917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-06-10T14:17:53.386172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-10T14:17:53.569036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-10T14:17:53.798581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-10T14:17:53.974330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-10T14:17:54.100077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-10T14:17:48.113167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-10T14:17:48.534580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

stateaccount_lengtharea_codenumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurninternational_plan_nointernational_plan_yesvoice_mail_plan_novoice_mail_plan_yes
00.320.5247930.0686270.4807690.7541960.6666670.7541830.5427550.5823530.5428660.6194940.5200000.6195840.5000.150.5000000.1111110.01.00.00.01.0
10.700.4380170.0686270.5000000.4597440.7454550.4596720.5375310.6058820.5376900.6440510.5885710.6443440.6850.150.6851850.1111110.01.00.00.01.0
20.620.5619830.0686270.0000000.6924610.6909090.6924360.3332420.6470590.3332250.4116460.5942860.4119300.6100.250.6092590.0000000.01.00.01.00.0
30.700.3429750.0000000.0000000.8517780.4303030.8517400.1701950.5176470.1701710.4984810.5085710.4985930.3300.350.3296300.2222220.00.01.01.00.0
40.720.3057850.0686270.0000000.4742530.6848480.4742300.4077540.7176470.4079590.4731650.6914290.4732700.5050.150.5055560.3333330.00.01.01.00.0
50.020.4834711.0000000.0000000.6355620.5939390.6355420.6065440.5941180.6066000.5162030.6742860.5166010.3150.300.3148150.0000000.00.01.01.00.0
60.380.4958681.0000000.4615380.6207680.5333330.6206490.9582070.6352940.9582660.5382280.6742860.5385480.3750.350.3759260.3333330.01.00.00.01.0
70.480.6033060.0686270.0000000.4466570.4787880.4466200.2834750.5529410.2834030.5362030.5485710.5362970.3550.300.3555560.0000000.00.01.01.00.0
80.360.4793390.0000000.0000000.5248930.5878790.5249330.9667310.4705880.9670010.5463290.5142860.5464270.4350.200.4351850.1111110.01.00.01.00.0
90.980.5785120.0686270.7115380.7357040.5090910.7356090.6103930.6529410.6104820.8263290.5542860.8266740.5600.250.5592590.0000000.00.01.00.01.0

Last rows

stateaccount_lengtharea_codenumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurninternational_plan_nointernational_plan_yesvoice_mail_plan_novoice_mail_plan_yes
49900.560.5743801.0000000.0000000.6961590.6969700.6961180.7110260.5941180.7110970.5855700.6400000.5858190.3750.300.3759260.1111111.01.00.01.00.0
49910.060.3966941.0000000.0000000.7186340.5393940.7185410.9356610.5352940.9359430.6493670.3828570.6494090.4400.250.4407410.1111111.01.00.01.00.0
49920.520.3388430.0686270.0000000.5357040.4242420.5356430.6703330.5176470.6703330.5410130.4514290.5413620.5150.300.5148150.0000000.01.00.01.00.0
49930.980.2975210.0000000.0000000.5061170.5393940.5060240.3607370.4823530.3607250.4713920.5085710.4715810.5750.300.5759260.3333330.01.00.01.00.0
49940.540.3057850.0000000.0000000.4856330.6121210.4856090.5309320.7411760.5308960.3268350.5942860.3269560.3450.350.3444440.1111110.01.00.01.00.0
49950.220.2024790.0000000.7692310.6705550.7696970.6705150.6131430.7411760.6133940.7531650.6628570.7535170.4950.250.4944440.2222220.01.00.00.01.0
49960.980.6239670.0686270.0000000.5240400.5454550.5239290.7060760.4294120.7062440.5407590.6457140.5407990.7350.100.7351850.3333331.01.00.01.00.0
49970.140.2479340.0686270.0000000.4000000.5393940.3999330.4751170.7529410.4752510.5377220.5542860.5379850.6800.200.6796300.1111110.01.00.01.00.0
49980.140.4462811.0000000.0000000.5371270.4060610.5371490.4720920.5411760.4720160.5681010.5085710.5683740.4250.300.4259260.0000000.01.00.01.00.0
49990.920.3512400.0686270.6538460.3681370.6181820.3681390.7343960.6117650.7343900.3918990.5714290.3922340.4650.800.4648150.0000000.01.00.00.01.0